SoroushVahidi/scidocs huggingface dataset:
Comparing two evolutionary algorithm based methods for layout generation: Dense packing versus subdivision
(2014)

We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms. We analyze the characteristics of the two methods on the basis of three floor plan scenarios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally, we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties.
A data-driven approach to cleaning large face datasets
(2014)

Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person. We formulate the problem of identifying the faces to be removed as a quadratic programming problem, which exploits the observations that faces of the same person should look similar, have the same gender, and normally appear at most once per image. Our results show that this method can reliably clean a large dataset, leading to a considerable reduction in the work needed to build it. Finally, we are releasing the FaceScrub dataset that was created using this approach. It consists of 141,130 faces of 695 public figures and can be obtained from http://vintage.winklerbros.net/facescrub.html.
AcuBot: a robot for radiological interventions
(2003)

We report the development of a robot for radiological percutaneous interventions using uniplanar fluoroscopy, biplanar fluoroscopy, or computed tomography (CT) for needle biopsy, radio frequency ablation, cryotherapy, and other needle procedures. AcuBot is a compact six-degree-of-freedom robot for manipulating a needle or other slender surgical instrument in the confined space of the imager without inducing image artifacts. Its distinctive characteristic is its decoupled motion capability correlated to the positioning, orientation, and instrument insertion steps of the percutaneous intervention. This approach allows each step of the intervention to be performed using a separate mechanism of the robot. One major advantage of this kinematic approach is patient safety. The first feasibility experiment performed with the robot, a cadaver study of perispinal blocks under biplanar fluoroscopy, is presented. The main expected application of this system is to CT-based procedures. AcuBot has received Food and Drug Administration clearance (IDE G010331/S1), and a clinical trial of using the robot for perispinal nerve and facet blocks is presently underway at Georgetown University, Washington, DC.
Visualizing the Hidden Activity of Artificial Neural Networks
(2017)

In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.
Forward and inverse kinematics model for robotic welding process using KR-16KS KUKA robot
(2011)

This paper aims to model the forward and inverse kinematics of a KUKA KR-16KS robotic arm in the application of a simple welding process. A simple welding task to weld a block onto a metal sheet is carried out in order to investigate the forward and inverse kinematics models of KR-16KS. A movement flow planning is designed and further developed into the KR-16KS programming. Eleven points of movement are studied for the forward kinematic modeling. A summary of calculation is obtained. A general D-H representation of forward and inverse matrix is obtained. This can be used in each of the welding operation movement based on KUKA KR-16KS robotic arm. A forward kinematic and an inverse kinematic aspect of KUKA KR-16KS is successfully modeled based on a simple welding task.
Vision based distance measurement system using single laser pointer design for underwater vehicle
(2009)

As part of a continuous research and development of underwater robotics technology at ITB, a visionbased distance measurement system for an Unmanned Underwater vehicle (UUV) has been designed. The proposed system can be used to predict horizontal distance between underwater vehicle and wall in front of vehicle. At the same time, it can be used to predict vertical distance between vehicle and the surface below it as well. A camera and a single laser pointer are used to obtain data needed by our algorithm. The vision-based navigation consists of two main processes which are the detection of a laser spot using image processing and the calculation of the distance based on laser spot position on the image.
Interactive Storytelling with Literary Feelings
(2007)

In this paper, we describe the integration of Natural Language Processing (NLP) within an emotional planner to support Interactive Storytelling. Our emotional planner is based on a standard HSP planner, whose originality is drawn from altering the agents’ beliefs and emotional states. Each character is driven by its own planner, while characters are able to operate on their reciprocal feelings thus affecting each other. Our baseline story is constituted by a classic XIX century French novel from Gustave Flaubert in which characters feelings play a dominant role. This approach benefits from the fact that Flaubert has described a specific ontology for his characters feelings. The objective of NLP should be to uncover from natural language utterances the same kind of affective elements, which requires an integration between NLP and the planning component at the level of semantic content. This research is illustrated with examples from a first fully integrated prototype comprising NLP, emotional planning and real-time 3D animation.
Decision Forests, Convolutional Networks and the Models in-Between
(2016)

This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters.
Janus: a general purpose WebRTC gateway
(2014)

This paper deals with the design and implementation of Janus, a general purpose, open source WebRTC gateway. Details will be provided on the architectural choices we took for Janus, as well as on the APIs we made available to extend and make use of it. Examples of how the gateway can be used for complex WebRTC applications are presented, together with some experimental results we collected during the development process.
Parallel online exact sum for Java8
(2016)

Java8 introduced the notion of streams that is a new data structure and supports multi-core processors. When the sum method is called for a stream of floating-point numbers, the summation is calculated at high-speed by applying MapReduce, which distributes computations to cores. However, since floating-point calculation causes an error, simple adaptation of this method can not determine the result uniquely. Then, in this study, we develop a summation program that can be applied to a stream with MapReduce. Our method can calculate at high-speed with keeping correctly rounded.
Factorial LDA: Sparse Multi-Dimensional Text Models
(2012)

Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors. Experiments on research abstracts show that our model can learn latent factors such as research topic, scientific discipline, and focus (methods vs. applications). Our modeling improvements reduce test perplexity and improve human interpretability of the discovered factors.
Model-Free Dual Heuristic Dynamic Programming
(2015)

Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one hidden layer for both the action and the critic networks design, and use delayed objective functions to train both the action and the critic networks online over time. We test both the MF-DHP and MB-DHP approaches with a discrete time example and a continuous time example under the same parameter settings. Our simulation results demonstrate that the MF-DHP approach can obtain a control performance competitive with that of the traditional MB-DHP approach while requiring less computational resources.
Impact of Smartphone Position on Sensor Values and Context Discovery
(2013)

With near-ubiquitous availability of smartphones equipped with a wide variety of sensors, research in building contextaware services has been growing. However, despite a large number of services proposed and developed as research prototypes, the number of truly context-aware applications available in smartphone applications market is quite limited. A major barrier for the large-scale proliferation of context aware applications is poor accuracy. This paper addresses one of the key reasons for this poor accuracy, which is the impact of smartphone positions. Users carry their smartphones in different positions such as holding in their hand, keeping inside their pants or jacket pocket, keeping in their purse, and so on. This paper addresses the issue of poor application accuracy due to varying smartphone positions. It first shows that smartphone positions significantly affect the values of the sensor data being collected by a context aware application, and this in turn has a significant impact on the accuracy of the application. Next, it describes the design and prototype development of a smartphone position discovery service that accurately detects a smartphone position. This service is based on the sensor data collected from carefully chosen sensors and runs orthogonal to any other context aware application or service. Finally, the paper demonstrates that the accuracy of an existing context aware service or application is significantly enhanced when run in conjunction with the proposed smartphone position discovery service.
An MPC/hybrid system approach to traction control
(2006)

This paper describes a hybrid model and a model predictive control (MPC) strategy for solving a traction control problem. The problem is tackled in a systematic way from modeling to control synthesis and implementation. The model is described first in the Hybrid Systems Description Language to obtain a mixed-logical dynamical (MLD) hybrid model of the open-loop system. For the resulting MLD model, we design a receding horizon finite-time optimal controller. The resulting optimal controller is converted to its equivalent piecewise affine form by employing multiparametric programming techniques, and finally experimentally tested on a car prototype. Experiments show that good and robust performance is achieved in a limited development time by avoiding the design of ad hoc supervisory and logical constructs usually required by controllers developed according to standard techniques.
TrajAnalytics: A Web-Based Visual Analytics Software of Urban Trajectory
(2017)

We present a web-based software, named TrajAnalytics, for the visual analytics of urban trajectory datasets. It allows users to interactively visualize and analyze the massive taxi trajectories over urban spaces. The software offers data management capability and enable various visual queries through a web interface. A set of visualization widgets and interaction functions are developed to promote easy user engagement. We implemented two prototypes with realworld datasets: (1) the origin/destination (OD) data of taxi trips collected in New York City; and (2) the taxi trajectory data collected in Porto City in Portugal. This open source software supports practitioners, researchers, and decision-makers in advancing transportation and urban studies in the new era of smart city.
Understanding of Antecedents to Achieve Customer Trust and Customer Intention to Purchase E-Commerce in Social Media , an Empirical Assessment
(2017)

Received Dec 9, 2016 Revised Mar 27, 2017 Accepted Apr 11, 2017 This study aims to analyze empirically three factors antecedents of trust they are system quality, information quality, and service quality. Customer trust is used in determining customer intention to purchase of e-commerce in social media (facebook). A number of respondents were 451. The results of this study concluded that three factors antecedents of trust directly had a positive impact to customer trust and indirectly had positive impact on customer intention to purchase in e-commerce transactions on social media. Keyword:
Cooperative Resource Management in Cloud-Enabled Vehicular Networks
(2015)

Cloud-enabled vehicular networks are a new paradigm to improve the quality of vehicular services, which have drawn considerable attention in industry and academia. In this paper, we consider the resource management and sharing problem for bandwidth and computing resources to support mobile applications in cloud-enabled vehicular networks. In such an environment, cloud service providers (SPs) can cooperate to form coalitions to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory for cooperation among cloud SPs to share their idle resources. As a result, the resources can be better utilized, and the QoS for users can be improved. Numerical results indicate that our scheme can improve resource utilization and increase by 75% the QoS of the applications compared with that without cooperation. Moreover, the higher service cost of cooperation brings negative effect on coalition formation. The higher cooperation willingness of cloud SPs and the lower service cost support more service applications.
A model to support sign language content development for digital television
(2009)

This paper proposes a model to support sign language content development and deployment in digital television scenarios. The model objective is to overcome traditional video limitations on the production of signed content and improve accessibility on digital television. Previous work on sign and gesture recognition, sign notation systems and avatar animation are reviewed. Sign communication requirements are investigated and adequate algorithms are identified to enable sign language processing and transmission. An application interface is proposed in order to access the model functionalities. The final discussion lists the benefits and limitations of the proposed model.
Recent Advancements in Event Processing
(2018)

Event processing (EP) is a data processing technology that conducts online processing of event information. In this survey, we summarize the latest cutting-edge work done on EP from both industrial and academic research community viewpoints. We divide the entire field of EP into three subareas: EP system architectures, EP use cases, and EP open research topics. Then we deep dive into the details of each subsection. We investigate the system architecture characteristics of novel EP platforms, such as Apache Storm, Apache Spark, and Apache Flink. We found significant advancements made on novel application areas, such as the Internet of Things; streaming machine learning (ML); and processing of complex data types such as text, video data streams, and graphs. Furthermore, there has been significant body of contributions made on event ordering, system scalability, development of EP languages and exploration of use of heterogeneous devices for EP, which we investigate in the latter half of this article. Through our study, we found key areas that require significant attention from the EP community, such as Streaming ML, EP system benchmarking, and graph stream processing.
A Single-Stage LED Driver Based on Interleaved Buck-boost Circuit and LLC Resonant Converter
(2016)

A single-stage LED driver based on interleaved buck-boost circuit and LLC resonant converter is proposed. The buck-boost circuit and LLC resonant converter are integrated by sharing switches, which can decrease the system cost and improve system efficiency. The input voltage of the buck-boost circuit is half of the rectified voltage, and two buckboost circuits are formed with the two half-bridge switches and corresponding diodes. The two buckboost circuits work in interleaved mode and the inductor current is in discontinuous conduction mode, both helping to achieve the power factor correction. The half-bridge LLC resonant converter is adopted here, and the soft switching characteristic of the LLC resonant converter is not changed by the switch integration. The primary-side switches still work in zero voltage switching (ZVS) mode, and the secondary diodes still work in zero current switching (ZCS) mode, which both reduce the switching losses and improve the efficiency of the system.
Design of an anthropomorphic prosthetic hand driven by Shape Memory Alloy actuators
(2008)

This paper presents the mechanical design of an ultra-lightweight, biomimetically actuated anthropomorphic hand for prosthetic purposes. The proposed design is based on an underactuated configuration of 16 joints and 7 active degrees of freedom. Shape Memory Alloy wires are used as motive elements of a specially designed actuation system installed within the envelope of the hand and forearm; due to their inherent contraction ability when heated, these innovative micro-actuators produce linear motion which is imparted to the fingers via a tendon transmission system. The overall design is completed with the integration of the necessary locking mechanisms for power saving. An experimental prototype of the suggested prosthesis has been fabricated using rapid prototyping techniques and it will be used as an evaluation platform for further research towards the development of a truly multifunctional, silent and cosmetically appealing hand for upper limb amputees.
A Study of Momentum Effects on the Swedish Stock Market using Time Series Regression
(2018)

This study investigates if momentum effects can be found on the Swedish stock market by testing a cross-sectional momentum strategy on historical data. To explain the results mathematically, a second approach, involving time series regression for predicting future returns is introduced and thereby extends the cross-sectional theory. The result of the study shows that momentum effects through the cross-sectional strategy exist on the Swedish stock market. Although positive return is found, the time series regression do not give any significance for predicting future returns. Hence, there is a contradiction between the two approaches.
Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing
(2002)

Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firm’s intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making.